Good developer prompts are reusable specifications, not one-off instructions. This library provides 25 adaptable prompt templates for code generation, debugging, documentation, SQL, JSON, regex, extraction, summarization, and technical analysis, with practical guidance for tailoring each pattern to a model, codebase, or data workflow.
Overview
Prompt engineering becomes more reliable when a prompt makes the task, context, constraints, and expected output explicit. A useful prompt template gives you a starting structure that can be reused across projects while leaving room for the details that change.
The patterns below are designed for developer workflows and AI development tasks. They can be adapted for ChatGPT, an API-based model, an internal assistant, or another AI tool. Treat the output as a draft or transformation unless your workflow includes validation. Generated code should be reviewed, queries should be tested against representative data, and extracted values should be checked against the source.
A strong prompt usually answers five questions:
- What is the task? State the action in one sentence.
- What context is available? Include code, schema, examples, documents, or error messages.
- What constraints apply? Specify language, version, security requirements, length, or allowed operations.
- What should the response contain? Define fields, formatting, and the level of explanation.
- How will the result be checked? Ask for tests, assumptions, edge cases, or validation notes.
For broader prompt design principles, see the practical prompt engineering guide and use a prompt testing framework when a prompt supports a repeated workflow.
Template structure
Use the following base structure when creating or adapting a developer prompt:
Role or perspective: You are [relevant technical role].
Task: [specific action and desired outcome].
Context: [code, schema, input, environment, or background].
Constraints: [versions, rules, exclusions, security, style, limits].
Output format: [exact structure, fields, or code block requirements].
Validation: [tests, assumptions, edge cases, or checks to include].Do not add every field mechanically. A short formatting request may need only a task and output format, while a production code change benefits from context, constraints, and validation. If the output must be consumed by software, request structured output such as JSON and define the schema clearly. If the task is ambiguous, instruct the model to list assumptions or ask focused clarification questions rather than silently inventing requirements.
How to customize
Replace bracketed placeholders with concrete information. Include the smallest useful input that allows the model to reason correctly, but do not omit details that affect the answer. For code, provide the relevant function, interfaces, dependencies, runtime, and error output. For data tasks, provide the schema, representative records, null-handling rules, and desired field types.
Separate instructions from input with labels such as CONTEXT, INPUT, and OUTPUT REQUIREMENTS. This makes prompts easier to read, test, and update. For recurring tasks, store the template in version control and keep a small set of representative examples. A few-shot prompting example can clarify the desired transformation, but the example should reflect the quality and edge cases expected in production.
Prefer measurable requirements over vague words. Replace “make it secure” with “validate the identifier, reject unexpected fields, avoid string-built SQL, and explain the error path.” Replace “summarize clearly” with “produce five bullet points, preserve named entities, and separate confirmed facts from open questions.” These details improve consistency without requiring a longer prompt every time.
Examples
The following 25 prompt templates form a compact prompt library. Adapt the placeholders to your project.
- Explain unfamiliar code:
Explain the following [language] code for a developer joining the project. Describe inputs, outputs, control flow, dependencies, side effects, and likely edge cases. Do not rewrite it. Code: [code] - Generate a function:
Write a [language] function named [name] that [behavior]. Inputs: [types]. Output: [type]. Follow [style or version] conventions, handle [edge cases], and include focused tests. - Refactor code:
Refactor this [language] code to improve [readability/performance/testability] without changing behavior. Preserve the public interface. Show the revised code, summarize changes, and identify any behavior that needs verification: [code] - Debug an error:
Diagnose this [language] error. Explain the likely cause, show the smallest safe fix, and list checks to confirm it. Environment: [versions]. Error: [message]. Relevant code: [code] - Review a pull request:
Review this change for correctness, regressions, security, maintainability, and missing tests. Rank findings by severity, cite the relevant lines or functions, and avoid comments that are only stylistic. Diff: [diff] - Create unit tests:
Generate [framework] unit tests for [function]. Cover normal inputs, boundaries, invalid inputs, null or empty values, and failure behavior. Do not mock behavior that the test is intended to verify. Code: [code] - Build an API client:
Implement a [language] client for [endpoint]. Include authentication placeholder, request validation, timeout handling, error mapping, retries only where safe, and an example call. API details: [method, URL, schema] - Document an API:
Convert these endpoint details into concise API documentation with purpose, method, parameters, authentication, request example, response schema, errors, and a curl example. Do not infer undocumented behavior: [details] - Write a migration plan:
Create a step-by-step migration plan from [old system] to [new system]. Include dependencies, reversible stages, data validation, downtime assumptions, rollback triggers, and an acceptance checklist. - Generate a database schema:
Propose a relational schema for [domain]. Define tables, columns, types, keys, constraints, indexes, and assumptions. Explain normalization choices and identify fields requiring review. - Write SQL:
Write [dialect] SQL to [goal]. Use only the tables and columns listed below. Handle nulls explicitly and avoid duplicate rows. Return the query, a short explanation, and assumptions. Schema: [schema] - Optimize SQL:
Review this [dialect] query for correctness and likely performance issues. Suggest a revised query, possible indexes, and validation steps. Do not recommend an index without explaining the access pattern. Query: [query] - Format SQL:
Format the following [dialect] SQL without changing its logic, identifiers, literals, or comments. Return only the formatted query: [SQL] - Transform JSON:
Transform the input JSON into the target schema. Preserve values unless a mapping is specified, report missing required fields, and return valid JSON only. Target schema: [schema]. Input: [JSON] - Validate JSON:
Inspect this JSON against the supplied rules. Return an array of issues with path, problem, and suggested correction. Do not rewrite valid values. Rules: [rules]. JSON: [JSON] - Design a JSON response:
Design a JSON schema for [use case]. Define required fields, types, allowed values, null behavior, and one valid example. Keep the schema stable for client consumers. - Generate regex:
Create a regular expression for [requirement]. State the intended regex flavor, provide passing and failing examples, and explain important limitations. Do not claim it validates more than the stated requirement. - Extract entities:
Extract [entity types] from the text. Return an array with the original span, normalized value, entity type, and confidence rationale. Use null when a value is not present. Text: [text] - Extract keywords:
Identify the most useful keywords from this text for [search, tagging, or classification]. Return [number] phrases, preserve important multiword terms, remove generic filler, and group close variants. Text: [text] - Summarize technical content:
Summarize this [ticket/design/log/report] for [audience]. Include problem, evidence, decisions, risks, unresolved questions, and next actions. Separate stated facts from interpretation. Text: [text] - Classify a request:
Classify the input into exactly one label from [labels]. Return JSON with label, brief evidence, and confidence level. If evidence is insufficient, use [fallback label]. Input: [text] - Analyze sentiment or tone:
Assess the tone of this message using [label set]. Return label, evidence phrase, and uncertainty note. Do not infer private traits or intent beyond the text. Message: [text] - Compare technical options:
Compare [options] for [use case] using criteria: [criteria]. Present a trade-off table, state assumptions, and recommend an option only if the criteria support it. - Plan an automation workflow:
Design an automation that moves data from [source] to [destination]. Describe triggers, transformations, validation, retries, duplicate handling, logging, permissions, and failure escalation. - Create a technical checklist:
Create a release checklist for [system or change]. Group items by code, data, security, observability, documentation, and rollback. Make each item verifiable and mark owner placeholders.
For extraction, classification, and sentiment tasks, define labels and validation criteria before relying on the result. The related guides on keyword extraction, sentiment analysis, and text classification provide useful patterns for those workflows.
When to update
Revisit a prompt when the underlying task changes, not merely because a model is updated. Update it when the programming language, framework, database dialect, API contract, input schema, security requirement, or publishing workflow changes. A prompt also needs review when users report recurring errors, when outputs require frequent manual correction, or when a new edge case appears in production data.
Keep a small evaluation set for important templates. Include ordinary cases, boundary cases, malformed inputs, and examples where the model should refuse to guess. Compare outputs after changing the prompt, model, or surrounding workflow. Record the prompt version, input, output, evaluator notes, and pass or fail result. This turns prompt editing into a repeatable process rather than a subjective preference.
For a practical maintenance routine, review critical templates on a planned schedule and review them immediately after a breaking schema or workflow change. Remove instructions that no longer apply, clarify ambiguous requirements, and keep examples synchronized with the expected output. When a prompt handles customer-facing content or sensitive operations, pair it with explicit guardrails and an escalation path, as discussed in the guide to prompt guardrails.
Start by selecting one repeated developer task, replacing the placeholders in the closest template, and saving three representative test inputs. Check the result manually, add the most useful constraints, and store the revised prompt with a version label. Over time, this simple practice turns scattered ChatGPT prompts into a maintainable prompt library for coding, data processing, and AI workflow automation.
